Abstract：
OBJECTIVE: Deep learning superresolution (SR) is a promising approach to reduce MRI scan time without requiring custom sequences or iterative reconstruction. Previous deep learning SR approaches have generated low-resolution training images by simple k-space truncation, but this does not properly model in-plane turbo spin echo (TSE) MRI resolution degradation, which has variable T2 relaxation effects in different k-space regions. To fill this gap, we developed a T2 -deblurred deep learning SR method for the SR of 3D-TSE images.
METHODS: A SR generative adversarial network was trained using physically realistic resolution degradation (asymmetric T2 weighting of raw high-resolution k-space data). For comparison, we trained the same network structure on previous degradation models without TSE physics modeling. We tested all models for both retrospective and prospective SR with 3 × 3 acceleration factor (in the two phase-encoding directions) of genetically engineered mouse embryo model TSE-MR images.
RESULTS: The proposed method can produce high-quality 3 × 3 SR images for a typical 500-slice volume with 6-7 mouse embryos. Because 3 × 3 SR was performed, the image acquisition time can be reduced from 15 h to 1.7 h. Compared to previous SR methods without TSE modeling, the proposed method achieved the best quantitative imaging metrics for both retrospective and prospective evaluations and achieved the best imaging-quality expert scores for prospective evaluation.
CONCLUSIONS: The proposed T2 -deblurring method improved accuracy and image quality of deep learning-based SR of TSE MRI. This method has the potential to accelerate TSE image acquisition by a factor of up to 9.
METHODS: A SR generative adversarial network was trained using physically realistic resolution degradation (asymmetric T2 weighting of raw high-resolution k-space data). For comparison, we trained the same network structure on previous degradation models without TSE physics modeling. We tested all models for both retrospective and prospective SR with 3 × 3 acceleration factor (in the two phase-encoding directions) of genetically engineered mouse embryo model TSE-MR images.
RESULTS: The proposed method can produce high-quality 3 × 3 SR images for a typical 500-slice volume with 6-7 mouse embryos. Because 3 × 3 SR was performed, the image acquisition time can be reduced from 15 h to 1.7 h. Compared to previous SR methods without TSE modeling, the proposed method achieved the best quantitative imaging metrics for both retrospective and prospective evaluations and achieved the best imaging-quality expert scores for prospective evaluation.
CONCLUSIONS: The proposed T2 -deblurring method improved accuracy and image quality of deep learning-based SR of TSE MRI. This method has the potential to accelerate TSE image acquisition by a factor of up to 9.
摘要：
目的：深度学习超分辨率（SR）是减少MRI扫描时间而无需自定义序列或迭代重建的一种有前途的方法。以前的深度学习SR方法通过简单的k空间截断生成低分辨率训练图像，但这并不能正确模拟平面内涡轮自旋回波（TSE）MRI分辨率下降，在不同的k空间区域具有可变的T2弛豫效应。为了填补这个空白，我们开发了一种T2去模糊的深度学习SR方法，用于3D-TSE图像的SR。
方法：使用物理现实分辨率退化（原始高分辨率k空间数据的不对称T2加权）训练SR生成对抗网络。为了比较，我们在没有TSE物理建模的情况下，在以前的退化模型上训练了相同的网络结构。我们使用基因工程小鼠胚胎模型TSE-MR图像的3×3加速因子（在两个相位编码方向上）测试了所有模型的回顾性和前瞻性SR。
结果：所提出的方法可以为具有6-7个小鼠胚胎的典型500片体积产生高质量的3×3SR图像。因为进行了3×3SR，图像采集时间可以从15小时减少到1.7小时。与以前没有TSE建模的SR方法相比，所提出的方法在回顾性和前瞻性评估中均取得了最佳的定量成像指标,在前瞻性评估中也取得了最佳的成像质量专家评分.
结论：提出的T2去模糊方法提高了基于深度学习的TSEMRISR的准确性和图像质量。该方法具有将TSE图像采集加速高达9倍的潜力。
方法：使用物理现实分辨率退化（原始高分辨率k空间数据的不对称T2加权）训练SR生成对抗网络。为了比较，我们在没有TSE物理建模的情况下，在以前的退化模型上训练了相同的网络结构。我们使用基因工程小鼠胚胎模型TSE-MR图像的3×3加速因子（在两个相位编码方向上）测试了所有模型的回顾性和前瞻性SR。
结果：所提出的方法可以为具有6-7个小鼠胚胎的典型500片体积产生高质量的3×3SR图像。因为进行了3×3SR，图像采集时间可以从15小时减少到1.7小时。与以前没有TSE建模的SR方法相比，所提出的方法在回顾性和前瞻性评估中均取得了最佳的定量成像指标,在前瞻性评估中也取得了最佳的成像质量专家评分.
结论：提出的T2去模糊方法提高了基于深度学习的TSEMRISR的准确性和图像质量。该方法具有将TSE图像采集加速高达9倍的潜力。
